Convolutional neural networks achieve good performance on various applications, which is also applied in document analysis.
For example, the winner of the recent DIBCO event [1]} uses the U-Net convolutional
network architecture for accurate pixel classification.
In [2]}, the fully convolutional neural network is applied at multiple image scales.
The deep encoder-decoder architecture is used for binarization in [3]}, [4]}.
A hierarchical deep supervised network is proposed in [5]} for document binarization, which achieves start-of-the-art performance on several benchmark data sets.
In [6]}, the Grid Long Short-Term Memory (Grid LSTM) network is used for binarization.
However, it achieves lower performance than Vo's method [5]}.
